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Procurement AI Assistant

Procurement AI Assistant is an open-source application for retail buyers that searches product catalogs using natural-language queries and Superlinked semantic search. Its documented stack includes a Python and FastAPI backend, a Next.js and TypeScript production frontend or a Streamlit local interface, and Qdrant vector search with an in-memory option.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Procurement AI Assistant — OpenViST project profile in Search & Research AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when working with a retail product catalog and you want to explore natural-language semantic search rather than submit only fixed keyword queries.

Who it is for

  • Retail buyers searching product catalogs using plain-language criteria.
  • Procurement and marketing teams assessing product and supplier attributes.
  • Developers building or adapting semantic search for procurement catalogs.

Documented uses

  • Find products by combining criteria such as price, product type and supplier reliability.
  • Search for products associated with low cost, higher revenue or other catalog metrics.
  • Explore supplier reliability, profit margins and return rates where those fields are present in the catalog.
Before you choose

Requirements & limits

  • The README specifies Python 3.11+ and an OpenAI API key; Google Cloud Platform is also listed among prerequisites.
  • Node.js 18+ is listed for the production frontend, while Streamlit is the local-development option.
  • Qdrant Cloud is described as optional, with an in-memory database available by default.
  • The sample CSV schema includes product identifiers, category, cost, retail price, profit margin, orders, return rate and supplier reliability.
  • The project is licensed under MIT.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the local Streamlit interface

Prepare the documented backend and launch the local Streamlit frontend.

Before you start

  • Python 3.11+
  • OpenAI API key and configuration values for the environment file
  • Streamlit
  • Product CSV data in the expected schema

Steps

  1. Clone the repository and enter its directory.

  2. Install backend dependencies with pip or uv.

  3. Copy .env.example to .env and configure the API keys and settings.

  4. Start the backend using the documented Make target or Superlinked server command, then run streamlit run st_app/app.py.

Expected outcome

The README lists a local Streamlit interface at http://localhost:8501 and a backend at http://localhost:8080.

Guide limitations

  • The repository README gives a placeholder clone URL using yourusername; use the repository URL shown in the source link.
  • The guide documents setup stages, not a verified execution or search result.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Procurement AI AssistantFind products by combining criteria such as price, product type and supplier reliability.Cloud, Web
arXiv Search APISearch for arXiv papers using natural-language queries and vector similarity.Cloud, Server, WebThe README identifies a hosted demo at https://docsearch.redisvl.com and provides a local setup path. Local setup instructions request OpenAI and Cohere API keys.
AlisiaAsk questions in a chat interface and receive responses informed by web search.Cloud, Self-hosted, Web
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
AmirLayegh/procurement-ai-assistant · Official homepage title · retained in its source language
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